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2 October 2019 Machine learning methods and classification of vegetation in Brest, France
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Abstract
Urban green land plays a special significant role in our life. Many previous studies have already proven the feasibility of satellite imagery processing for urban green land detection, and many classification techniques were tested for this purpose. In this paper, two methods of Machine learning, such as Artificial Neural Networks (ANN) and Random Forest (RF) were tested on a series of very high spatial resolution satellite imagery to classify, highlight urban green lands and eventually study their change in three years. Firstly, we present Multilayer Perceptron (MLP), a simple feed-forward neural network which consists of two steps, SLIC superpixel segmentation and image classification through MLP trained model. Secondly, RF technique was applied and compared to the previous classification. Then the classification results were interpreted at the final stage. These methods are performed on satellite images Pléiades of the city of Brest (3 images acquired in 2016, 2017 and 2018), these multispectral images are already preprocessed and all pan-sharpened to 50 cm spatial resolution.
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© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Guanyao Xie, Simona Niculescu, Chinguyen Lam, and Elise Seveno "Machine learning methods and classification of vegetation in Brest, France", Proc. SPIE 11157, Remote Sensing Technologies and Applications in Urban Environments IV, 111570J (2 October 2019); https://doi.org/10.1117/12.2533436
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